Improving loss ratios and profitability

Triad Analytic Solutions helps clients perform advanced analytics

The property and casualty insurance industry is an extremely competitive marketplace. For smaller insurance carriers, success—and survival—requires at a minimum the ability to accurately segment customers by their loss potential and then use that information to price policies competitively. In today's market, competitor pricing incorporates many forms of sophisticated analyses, based on vast amounts of clean and accurate business and customer data.

No one knows this better than Chris Hardin and Brian Scott, both managing partners at Triad Analytic Solutions. Hardin and Scott have parlayed their years of experience in the property and casualty insurance industry into a business geared toward helping small- and mid-sized carriers with limited or no dedicated statistical expertise or data management capabilities. By engaging Triad, clients gain access to the pricing and product expertise found in the analytics departments of many large carriers. Triad facilitates this by providing outsourced data integration and analytical services using SAS® Business Analytics.

In terms of loss ratios and profitability, we can show our clients how they can improve their performance ... With SAS, we can show and quantify for them, based on historical losses, how they could have had better profitability

Brian Scott, ACAS, MAAA
Managing Partner

Getting to the data

"Small to mid-size insurance carriers need to have data in a singular, unified format for them to be able to apply advanced analytics, which in turn enables them to compete in an environment that relies heavily on accurate segmentation," explains Hardin, a career statistician. "We're giving these companies a way to bypass their busy IT departments to get at the data quickly and perform analysis. Many have never been able to do this before and have almost given up asking IT, because by the time they get the data they need it's often too late."

According to the partners, Triad's services often begin with integrating multiple databases, which typically house critical premium and loss data, to perform the required analytical work. Once the data integration work is done, the consolidated data marts and automated processes—which ensure that the data is constantly refreshed and accurate—are handed over to clients, with the training that allows clients to perform the work themselves in the future.

"In one engagement, the client was in the midst of a multi-year, seven-figure project with an IT vendor, which was designed to deliver critical data access," recounts Hardin. "In a matter of months, we were able to use SAS to build and query an interim database that yielded similar actionable analytic pricing information. We also trained the employees to maintain and use the database we created. It's a very satisfying feeling building the tools that empower a company's employees to help themselves going forward."

"It's expensive for small- and mid-sized carriers to hire full-time actuaries and statisticians, and it can be difficult to find employees with detailed knowledge about the spectrum of distribution channels and customer segments—there's no substitute for experience," adds Scott. "It's also hard to attract employees to certain locations. Our value is that we can go into these companies to assist them and train their staff to perform the work after we're gone."

The competitive advantage

And once the data is ready, Triad's customers can begin applying advanced analytics to drive quantifiable competitive advantages.

"Our clients need to get their overall rating plans stabilized," says Hardin. "From there they can take analytics as far as they want to. They can move toward understanding price elasticity and retention and more advanced topics. It's a process that builds on itself – the analysis used to solve basic but elusive questions often creates the analytic foundation necessary to resolve other more complex issues as well."

"There are many small- to mid-size carriers still pricing policies based on univariate analysis," he continues. "They look at results one variable at a time and make decisions on how to treat the information. The real benefit in pricing comes from multivariate analysis, which helps you understand the impact of all the interactions between different pricing variables. We've dealt with just about every analytic data vendor in the insurance industry, so we have a pretty good idea what is out there. Typically, the proprietary carrier models that Triad builds using SAS offer more lift than the generic ones sold by certain vendors. And by maintaining their own models, carriers can save money versus purchasing one, which can nearly double the cost.

"Some of the lifetime value retention and premium modeling we've implemented has seen monoline auto renewal rate improvements of several points—a result that could easily yield a seven-figure improvement to a carrier's top line over time," he adds.

Segmenting high and low risk business

“Auto insurance is a very competitive business,” adds Hardin. “The companies that can perform advanced high and low risk segmentation are going to have a leg up on their competition, and thrive by more accurately surcharging and discounting their business, commensurate with the risk that accompanies it. And, if the company isn’t using certain rating variables—such as credit and insurance scores—they will miss out on the additional benefits segmentation can provide, such as improved retention and lower acquisition and servicing costs as well.”

"In terms of loss ratios and profitability, we can show our clients how they can improve their performance with better segmentation and pricing," explains Scott. "With SAS, we can not only segment risk and build rating plans more accurately, but also show and quantify for our clients, using historical data, how they could have had better profitability."

So why use SAS?

"SAS is a flexible tool," Scott concludes. "It reads and handles lots of different data formats and allows us to go from data integration to statistical techniques without changing software. SAS provides a quick way to go back and forth between these two worlds. There are other tools out there, but they can't function with the large data sets that we're using, nor can they transition so easily to the statistical work."

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